In this paper, we advocate a third direction, which falls between the two described extremes and aims to address their shortcomings.
The proposed method is unsupervised in the sense that no solved instances of the power allocation problem are needed for training.
However, our approach still imitates classical methods by incorporating part of their structure into the layered architecture of the neural network.
The goal is to leverage the theoretical models developed with expert knowledge and achieve near-optimal performance with significantly reduced execution time.
To accomplish this, we follow the paradigm of algorithm unfolding [1]}.
